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PrincipalComponent
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0
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Higher Dimensional RNASeq Clustering Significance
DESeq2
RNASeq
PrincipalComponent
pcaExplorer
written 17 months ago by
James
• 0
0
votes
2
replies
746
views
Using Multi-Dimensional Scaling (MDS) to produce a vector in order to account for patient bias when constructing DGE lists from RNA-seq datasets in R?
PrincipalComponent
StatisticalMethod
edgeR
MultidimensionalScaling
Normalization
14 months ago
rcreswell01
• 0
0
votes
2
replies
315
views
PCA after DESeq2
DESeq2
PrincipalComponent
prin
DifferentialExpression
updated 3 months ago by
Michael Love
42k • written 3 months ago by
Shaimaa Gamal
• 0
1
vote
1
reply
848
views
PCA rand heatmap of raw data
DESeq2
DiffSeq
PrincipalComponent
rlog
heatmaps
updated 3.2 years ago by
Michael Love
42k • written 3.2 years ago by
Fischer-philipp
▴ 30
0
votes
1
reply
903
views
DiffBind: dba.plotPCA() ellipse
PrincipalComponent
DiffBind
updated 22 months ago by
swbarnes2
★ 1.4k • written 22 months ago by
Lisa
• 0
0
votes
1
reply
443
views
How to extract proteins from PCs in plot_pca in DEP package
DEP
PrincipalComponent
PCA
plot_pca
updated 13 months ago by
Basti
▴ 780 • written 13 months ago by
fuqichen1
• 0
6 results • Page
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Comment: How to use bootRanges to bootstrap small RNA loci (nullranges package)
by
Poonam
• 0
Yes, blockLength=200000, and seg=seg. I am concerned about the inter-feature distance of features. I generated this plot for various bloc…
Answer: Can I trust the differential expression analysis when the two groups overlap on
by
Michael Love
42k
Can you use `vst` instead of `rlog` and increase to 1000 or 2000 genes? I'm wondering if there's a way to characterize these 7 samples. …
Comment: Can I trust the differential expression analysis when the two groups overlap on
by
BioinfGuru
▴ 30
I think I am about to run into the same issue with my data, so I'll be interested to read peoples opinions on this also i.e. when the group…
Answer: How to approach time course RNAseq data? Seeing 80% DE genes in timepoint groups
by
Gordon Smyth
50k
The MDS plot shows that the time points are hugely different and whereas the replicate samples appear very consistent, with very low variat…
Answer: Store output of matchPWM()
by
mat149
▴ 70
hitlist<-lapply(irs, FUN = matchPWM, min.score="90%", with.score=TRUE, pwm = pwm) ot <- endoapply(hitlist, function(x) as(x, "IRanges")) …
Votes
Comment: Store output of matchPWM()
Answer: Store output of matchPWM()
Answer: Store output of matchPWM()
Answer: ChipQC 'names' attribute [9] must be the same length as the vector [2]'
ChipQC 'names' attribute [9] must be the same length as the vector [2]'
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